关键词: 高维数据, 异同性, 变系数模型, 整合分析

Abstract: With the development of information technology, high-dimensional data has become increasingly rich. In reality, a lot of high-dimensional data is a mixture of multiple datasets with heterogeneous sources or subjects. The identification of commonality and difference among high-dimensional datasets has become one of the goals of big data analysis. This paper proposes a novel integrative analysis method for high-dimensional data based on a varying-coefficient model. It can simultaneously conduct variable selection, coefficient estimation, and, most importantly, automatically identify the commonality and difference among multiple datasets. Simulations demonstrate that the proposed method outperforms alternative methods in commonality identification, variable selection, coefficient estimation, and forecast. Finally, the proposed method is applied to lung cancer datasets and biologically meaningful pathogenic genes are identified as well as the commonality and difference of two sub-types.

Key words: High Dimensional Data, Commonality and Difference, Varying-coefficient Model, Integrative Analysis